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Install in OpenClaw
/install crypto-self-learning
Description
Self-learning system for crypto trading. Logs trades with full context (indicators, market conditions), analyzes patterns of wins/losses, and auto-updates trading rules. Use to log trades, analyze performance, identify what works/fails, and continuously improve trading accuracy.
Usage Guidance
Install only if you want an agent to keep local crypto trade records and persist learned rules. Review what it writes to MEMORY.md, keep trade logs free of secrets or exchange credentials, and prefer running memory updates only after explicit user approval.
Capability Analysis
Type: OpenClaw Skill
Name: crypto-self-learning
Version: 1.0.0
The OpenClaw skill 'crypto-self-learning' is designed for local crypto trade analysis and rule generation. All scripts (`analyze.py`, `generate_rules.py`, `log_trade.py`, `update_memory.py`) operate on local JSON files within the skill's `data` directory. The `SKILL.md` instructions and the `update_memory.py` script's modification of `MEMORY.md` are directly aligned with the stated purpose of updating the agent's learned rules, without any evidence of prompt injection, data exfiltration, remote execution, or other malicious behaviors. Required binaries (`jq`, `python3`) are standard and appropriate for the task.
Capability Assessment
Purpose & Capability
Local trade logging, analysis, rule generation, and learned-rule updates fit the stated crypto self-learning purpose. The supplied telemetry does not show wallet access, live trading authority, credential use, remote execution, or exfiltration.
Instruction Scope
The skill instructs the agent to read and write local files, including trade records and MEMORY.md. That is purpose-aligned, but users should understand that running the memory update changes future guidance.
Install Mechanism
No separate install-time behavior, package installation, startup hook, or background service was identified in the supplied artifact evidence. SkillSpector's metadata-permission concern is a transparency note rather than evidence of hidden behavior.
Credentials
Use of python3 and jq, local JSON files, and a skill data directory is proportionate for local analysis and rule generation.
Persistence & Privilege
Appending learned rules to MEMORY.md is persistent agent-behavior influence. It is disclosed and aligned with the self-learning purpose, but should remain user-directed and scoped.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install crypto-self-learning - After installation, invoke the skill by name or use
/crypto-self-learning - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Self-learning system for crypto trading. Logs trades, analyzes patterns, generates rules, and auto-updates agent memory for continuous improvement.
Metadata
Frequently Asked Questions
What is Crypto Self-Learning?
Self-learning system for crypto trading. Logs trades with full context (indicators, market conditions), analyzes patterns of wins/losses, and auto-updates trading rules. Use to log trades, analyze performance, identify what works/fails, and continuously improve trading accuracy. It is an AI Agent Skill for Claude Code / OpenClaw, with 6559 downloads so far.
How do I install Crypto Self-Learning?
Run "/install crypto-self-learning" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Crypto Self-Learning free?
Yes, Crypto Self-Learning is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Crypto Self-Learning support?
Crypto Self-Learning is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Crypto Self-Learning?
It is built and maintained by totaleasy (@totaleasy); the current version is v1.0.0.
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